{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Dataflowr](https://raw.githubusercontent.com/dataflowr/website/master/_assets/dataflowr_logo.png)](https://dataflowr.github.io/website/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cKEoXRSzUUCv"
   },
   "source": [
    "# Collaborative filtering\n",
    "-----\n",
    "\n",
    "In this example, we'll build a quick explicit feedback recommender system: that is, a model that takes into account explicit feedback signals (like ratings) to recommend new content.\n",
    "\n",
    "We'll use an approach first made popular by the [Netflix prize](https://en.wikipedia.org/wiki/Netflix_Prize) contest: [matrix factorization](https://datajobs.com/data-science-repo/Recommender-Systems-[Netflix].pdf). \n",
    "\n",
    "The basic idea is very simple:\n",
    "\n",
    "1. Start with user-item-rating triplets, conveying the information that user _i_ gave some item _j_ rating _r_.\n",
    "2. Represent both users and items as high-dimensional vectors of numbers. For example, a user could be represented by `[0.3, -1.2, 0.5]` and an item by `[1.0, -0.3, -0.6]`.\n",
    "3. The representations should be chosen so that, when we multiplied together (via [dot products](https://en.wikipedia.org/wiki/Dot_product)), we can recover the original ratings.\n",
    "4. The utility of the model then is derived from the fact that if we multiply the user vector of a user with the item vector of some item they _have not_ rated, we hope to obtain a prediction for the rating they would have given to it had they seen it.\n",
    "\n",
    "![collaborative filtering](matrix_factorization.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OeHGO4qbUUC4"
   },
   "source": [
    "## 1. Preparations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "frj5rX9wUUC_"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os.path as op\n",
    "from pathlib import Path\n",
    "\n",
    "from zipfile import ZipFile\n",
    "try:\n",
    "    from urllib.request import urlretrieve\n",
    "except ImportError:  # Python 2 compat\n",
    "    from urllib import urlretrieve\n",
    "\n",
    "# this line needs to be modified if not on colab:\n",
    "data_folder = '/content/'\n",
    "# for example something like:\n",
    "#ROOT_DIR = Path.home()\n",
    "#data_folder = op.join(ROOT_DIR,'data/')\n",
    "\n",
    "ML_100K_URL = \"http://files.grouplens.org/datasets/movielens/ml-100k.zip\"\n",
    "ML_100K_FILENAME = op.join(data_folder,ML_100K_URL.rsplit('/', 1)[1])\n",
    "ML_100K_FOLDER = op.join(data_folder,'ml-100k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "pK7DyF_ZUUDQ"
   },
   "source": [
    "We start with importing a famous dataset, the [Movielens 100k dataset](https://grouplens.org/datasets/movielens/100k/). It contains 100,000 ratings (between 1 and 5) given to 1682 movies by 943 users:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tKfA7pagUUDT"
   },
   "outputs": [],
   "source": [
    "if not op.exists(ML_100K_FILENAME):\n",
    "    print('Downloading %s to %s...' % (ML_100K_URL, ML_100K_FILENAME))\n",
    "    urlretrieve(ML_100K_URL, ML_100K_FILENAME)\n",
    "\n",
    "if not op.exists(ML_100K_FOLDER):\n",
    "    print('Extracting %s to %s...' % (ML_100K_FILENAME, ML_100K_FOLDER))\n",
    "    ZipFile(ML_100K_FILENAME).extractall(data_folder)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LKXSFZpUUUDl"
   },
   "source": [
    "Other datasets, see: [Movielens](https://grouplens.org/datasets/movielens/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hHgLg4jBUUDp"
   },
   "source": [
    "## 2. Data analysis and formating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZlGyPSBpUUDt"
   },
   "source": [
    "[Python Data Analysis Library](http://pandas.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QFRB1aAPUUDy"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>186</td>\n",
       "      <td>302</td>\n",
       "      <td>3</td>\n",
       "      <td>891717742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>377</td>\n",
       "      <td>1</td>\n",
       "      <td>878887116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "      <td>880606923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>166</td>\n",
       "      <td>346</td>\n",
       "      <td>1</td>\n",
       "      <td>886397596</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  ratings  timestamp\n",
       "0      196      242        3  881250949\n",
       "1      186      302        3  891717742\n",
       "2       22      377        1  878887116\n",
       "3      244       51        2  880606923\n",
       "4      166      346        1  886397596"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "all_ratings = pd.read_csv(op.join(ML_100K_FOLDER, 'u.data'), sep='\\t',\n",
    "                          names=[\"user_id\", \"item_id\", \"ratings\", \"timestamp\"])\n",
    "all_ratings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "enyJuYhUUUEL"
   },
   "source": [
    "Let's check out a few macro-stats of our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oS_aeGxwj62g"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  item_id          item_name\n",
       "0       1   Toy Story (1995)\n",
       "1       2   GoldenEye (1995)\n",
       "2       3  Four Rooms (1995)\n",
       "3       4  Get Shorty (1995)\n",
       "4       5     Copycat (1995)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_movies_names = []\n",
    "list_item_ids = []\n",
    "with open(op.join(ML_100K_FOLDER, 'u.item'), encoding = \"ISO-8859-1\") as fp:\n",
    "    for line in fp:\n",
    "        list_item_ids.append(line.split('|')[0])\n",
    "        list_movies_names.append(line.split('|')[1])\n",
    "        \n",
    "movies_names = pd.DataFrame(list(zip(list_item_ids, list_movies_names)), \n",
    "               columns =['item_id', 'item_name']) \n",
    "movies_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NH-kq2rHj62j"
   },
   "outputs": [],
   "source": [
    "movies_names['item_id']=movies_names['item_id'].astype(int)\n",
    "all_ratings['item_id']=all_ratings['item_id'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kkFnwTUTj62m"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>item_id</th>\n",
       "      <th>ratings</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>item_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>196</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>881250949</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>63</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>875747190</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>226</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>883888671</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>154</td>\n",
       "      <td>242</td>\n",
       "      <td>3</td>\n",
       "      <td>879138235</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>306</td>\n",
       "      <td>242</td>\n",
       "      <td>5</td>\n",
       "      <td>876503793</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  item_id  ratings  timestamp     item_name\n",
       "0      196      242        3  881250949  Kolya (1996)\n",
       "1       63      242        3  875747190  Kolya (1996)\n",
       "2      226      242        5  883888671  Kolya (1996)\n",
       "3      154      242        3  879138235  Kolya (1996)\n",
       "4      306      242        5  876503793  Kolya (1996)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings = all_ratings.merge(movies_names,on='item_id')\n",
    "all_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3M7-jinQUUEO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of entries\n",
    "len(all_ratings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ofVcE781UUEa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.000000\n",
       "mean          3.529860\n",
       "std           1.125674\n",
       "min           1.000000\n",
       "25%           3.000000\n",
       "50%           4.000000\n",
       "75%           4.000000\n",
       "max           5.000000\n",
       "Name: ratings, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings['ratings'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tCdV2qzMUUEk"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of unique rating values\n",
    "len(all_ratings['ratings'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zU9vARwJUUEs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.00000\n",
       "mean        462.48475\n",
       "std         266.61442\n",
       "min           1.00000\n",
       "25%         254.00000\n",
       "50%         447.00000\n",
       "75%         682.00000\n",
       "max         943.00000\n",
       "Name: user_id, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings['user_id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P1QoS71CUUE7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "943\n"
     ]
    }
   ],
   "source": [
    "# number of unique users\n",
    "total_user_id = len(all_ratings['user_id'].unique())\n",
    "print(total_user_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l3gfokbNUUFH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    100000.000000\n",
       "mean        425.530130\n",
       "std         330.798356\n",
       "min           1.000000\n",
       "25%         175.000000\n",
       "50%         322.000000\n",
       "75%         631.000000\n",
       "max        1682.000000\n",
       "Name: item_id, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_ratings['item_id'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pE6-aj0KUUFY"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1682\n"
     ]
    }
   ],
   "source": [
    "# number of unique rated items\n",
    "total_item_id = len(all_ratings['item_id'].unique())\n",
    "print(total_item_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AiyZLRqVj627"
   },
   "outputs": [],
   "source": [
    "all_ratings['item_id'] = all_ratings['item_id'].apply(lambda x :x-1)\n",
    "all_ratings['user_id'] = all_ratings['user_id'].apply(lambda x :x-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oiujn3oZj629"
   },
   "outputs": [],
   "source": [
    "movies_names['item_id']=movies_names['item_id'].apply(lambda x: x-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "FH6lv18Oj63A"
   },
   "outputs": [],
   "source": [
    "movies_names=movies_names.set_index('item_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1d_laQhNj63D"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>item_id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 item_name\n",
       "item_id                   \n",
       "0         Toy Story (1995)\n",
       "1         GoldenEye (1995)\n",
       "2        Four Rooms (1995)\n",
       "3        Get Shorty (1995)\n",
       "4           Copycat (1995)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_names.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ur4bjuniUUFj"
   },
   "source": [
    "For spliting the data into _train_ and _test_ we'll be using a pre-defined function from [scikit-learn](http://scikit-learn.org/stable/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZT5oxhoGUUFm"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "ratings_train, ratings_test = train_test_split(\n",
    "    all_ratings, test_size=0.2, random_state=42)\n",
    "\n",
    "user_id_train = ratings_train['user_id']\n",
    "item_id_train = ratings_train['item_id']\n",
    "rating_train = ratings_train['ratings']\n",
    "\n",
    "user_id_test = ratings_test['user_id']\n",
    "item_id_test = ratings_test['item_id']\n",
    "rating_test = ratings_test['ratings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aLem0iAjUUFs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(user_id_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "45HhGRbFUUF2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "943"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(user_id_train.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "z9m6gSoKUUF_"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1656"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(item_id_train.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vmx1YZmTUUGG"
   },
   "source": [
    "We see that all the movies are not rated in the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TYJk2PQmj63M"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1155 Cyclo (1995)\n",
      "1541 Scarlet Letter, The (1926)\n",
      "1561 Eye of Vichy, The (Oeil de Vichy, L') (1993)\n",
      "1564 Daens (1992)\n",
      "1565 Man from Down Under, The (1943)\n",
      "1567 Vermont Is For Lovers (1992)\n",
      "1570 Touki Bouki (Journey of the Hyena) (1973)\n",
      "1446 Century (1993)\n",
      "1451 Lady of Burlesque (1943)\n",
      "1452 Angel on My Shoulder (1946)\n",
      "1582 Invitation, The (Zaproszenie) (1986)\n",
      "1328 Low Life, The (1994)\n",
      "1457 Damsel in Distress, A (1937)\n",
      "1589 To Have, or Not (1995)\n",
      "1338 Stefano Quantestorie (1993)\n",
      "1598 Someone Else's America (1995)\n",
      "1471 Visitors, The (Visiteurs, Les) (1993)\n",
      "1603 He Walked by Night (1948)\n",
      "1613 Reluctant Debutante, The (1958)\n",
      "1492 Modern Affair, A (1995)\n",
      "856 Paris Was a Woman (1995)\n",
      "1625 Nobody Loves Me (Keiner liebt mich) (1994)\n",
      "1640 Dadetown (1995)\n",
      "1519 Fear, The (1995)\n",
      "1524 Object of My Affection, The (1998)\n",
      "1658 Getting Away With Murder (1996)\n"
     ]
    }
   ],
   "source": [
    "movies_not_train = (set(all_ratings['item_id']) -set(item_id_train))\n",
    "for m in movies_not_train:\n",
    "    print(m,movies_names.loc[m]['item_name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TOY6YCqPUUGI"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75220    434\n",
       "48955    338\n",
       "44966    847\n",
       "13568    751\n",
       "92727    177\n",
       "Name: user_id, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "user_id_train.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ZCYvrYhUUGP"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75220    423\n",
       "48955    210\n",
       "44966    185\n",
       "13568    337\n",
       "92727     75\n",
       "Name: item_id, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_id_train.iloc[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qZsssRYSUUGW"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75220    1\n",
       "48955    5\n",
       "44966    5\n",
       "13568    3\n",
       "92727    3\n",
       "Name: ratings, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rating_train.iloc[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Q7JF-d05UUGc"
   },
   "source": [
    "## 3. The model\n",
    "\n",
    "We can feed our dataset to the `FactorizationModel` class - a sklearn-like object that allows us to train and evaluate the explicit factorization models.\n",
    "\n",
    "Internally, the model uses the `Model_dot`(class to represents users and items. It's composed of a 4 `embedding` layers:\n",
    "\n",
    "- a `(num_users x latent_dim)` embedding layer to represent users,\n",
    "- a `(num_items x latent_dim)` embedding layer to represent items,\n",
    "- a `(num_users x 1)` embedding layer to represent user biases, and\n",
    "- a `(num_items x 1)` embedding layer to represent item biases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tsrfFi1QUUGd"
   },
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iaMRfQoCj63W"
   },
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vaf7-kdnUUGh"
   },
   "source": [
    "Let's generate [Embeddings](http://pytorch.org/docs/master/nn.html#embedding) for the users, _i.e._ a fixed-sized vector describing the user"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2iu-1X4AUUGi"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[-0.1428,  0.4004,  0.2689],\n",
       "         [ 0.3936, -0.1664, -0.6563],\n",
       "         [ 0.6233, -0.5290,  0.9420],\n",
       "         [ 0.3603, -1.1287, -1.9112]],\n",
       "\n",
       "        [[ 0.6233, -0.5290,  0.9420],\n",
       "         [ 1.2512,  0.2615,  0.6053],\n",
       "         [ 0.3936, -0.1664, -0.6563],\n",
       "         [ 0.5397, -0.0984, -0.7737]]], grad_fn=<EmbeddingBackward0>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "embedding_dim = 3\n",
    "embedding_user = nn.Embedding(total_user_id, embedding_dim)\n",
    "input = torch.LongTensor([[1,2,4,5],[4,3,2,0]])\n",
    "embedding_user(input)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HpkKu7PVUUGp"
   },
   "source": [
    "We will use some custom embeddings and dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eB6_y1nMUUGq"
   },
   "outputs": [],
   "source": [
    "class ScaledEmbedding(nn.Embedding):\n",
    "    \"\"\"\n",
    "    Embedding layer that initialises its values\n",
    "    to using a normal variable scaled by the inverse\n",
    "    of the embedding dimension.\n",
    "    \"\"\"\n",
    "    def reset_parameters(self):\n",
    "        \"\"\"\n",
    "        Initialize parameters.\n",
    "        \"\"\"\n",
    "        self.weight.data.normal_(0, 1.0 / self.embedding_dim)\n",
    "        if self.padding_idx is not None:\n",
    "            self.weight.data[self.padding_idx].fill_(0)\n",
    "\n",
    "\n",
    "class ZeroEmbedding(nn.Embedding):\n",
    "    \"\"\"\n",
    "    Used for biases.\n",
    "    \"\"\"\n",
    "    def reset_parameters(self):\n",
    "        \"\"\"\n",
    "        Initialize parameters.\n",
    "        \"\"\"\n",
    "        self.weight.data.zero_()\n",
    "        if self.padding_idx is not None:\n",
    "            self.weight.data[self.padding_idx].fill_(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ktXRW3-4UUGt"
   },
   "outputs": [],
   "source": [
    "class DotModel(nn.Module):\n",
    "    \n",
    "    def __init__(self,\n",
    "                 num_users,\n",
    "                 num_items,\n",
    "                 embedding_dim=32):\n",
    "        \n",
    "        super(DotModel, self).__init__()\n",
    "        \n",
    "        self.embedding_dim = embedding_dim\n",
    "        \n",
    "        self.user_embeddings = ScaledEmbedding(num_users, embedding_dim)\n",
    "        self.item_embeddings = ScaledEmbedding(num_items, embedding_dim)\n",
    "        self.user_biases = ZeroEmbedding(num_users, 1)\n",
    "        self.item_biases = ZeroEmbedding(num_items, 1)\n",
    "                \n",
    "        \n",
    "    def forward(self, user_ids, item_ids):\n",
    "        \n",
    "        user_embedding = self.user_embeddings(user_ids)\n",
    "        item_embedding = self.item_embeddings(item_ids)\n",
    "\n",
    "        user_bias = self.user_biases(user_ids).squeeze()\n",
    "        item_bias = self.item_biases(item_ids).squeeze()\n",
    "\n",
    "        dot = (user_embedding * item_embedding).sum(1)\n",
    "\n",
    "        return dot + user_bias + item_bias\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PlbbwU1ze5d7"
   },
   "outputs": [],
   "source": [
    "net = DotModel(total_user_id,total_item_id).to(device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ysX9Q9pxiMG4"
   },
   "source": [
    "Now test your network on a small batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Cp2ZOCFhfERq"
   },
   "outputs": [],
   "source": [
    "batch_users_np = user_id_train.values[:5].astype(np.int32)\n",
    "batch_items_np = item_id_train.values[:5].astype(np.int32)\n",
    "batch_ratings_np = rating_train[:5].values.astype(np.float32)\n",
    "batch_users_tensor = torch.LongTensor(batch_users_np).to(device)\n",
    "batch_items_tensor = torch.LongTensor(batch_items_np).to(device)\n",
    "batch_ratings_tensor = torch.tensor(batch_ratings_np).to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8K4btQb2gsAg"
   },
   "outputs": [],
   "source": [
    "predictions = net(batch_users_tensor,batch_items_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I20UhBLZjBEs"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 0.0004, -0.0115, -0.0022, -0.0041,  0.0098], device='cuda:0',\n",
       "       grad_fn=<AddBackward0>)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZGG3SBS9jizs"
   },
   "source": [
    "We will use MSE loss defined below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7sqlTLCyiwzl"
   },
   "outputs": [],
   "source": [
    "def regression_loss(predicted_ratings, observed_ratings):\n",
    "    return ((observed_ratings - predicted_ratings) ** 2).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wK5LqsRXi0_G"
   },
   "outputs": [],
   "source": [
    "loss_fn = regression_loss\n",
    "loss = loss_fn(predictions, batch_ratings_tensor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ins38kThjNl0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(13.8205, device='cuda:0', grad_fn=<MeanBackward0>)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MPpfdFaikudZ"
   },
   "source": [
    "Check that your network is learning by overfitting your network on this small batch (you should reach a loss below 0.5 in the cell below)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TcIY0t-xkFwT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(13.8110, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(11.7420, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(7.8395, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(4.2112, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(1.2794, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(0.4522, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(1.7970, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(2.6509, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(1.8255, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(0.8809, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(0.4742, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(0.5325, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(0.8673, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(1.0129, device='cuda:0', grad_fn=<MeanBackward0>)\n",
      "tensor(0.8527, device='cuda:0', grad_fn=<MeanBackward0>)\n"
     ]
    }
   ],
   "source": [
    "net = DotModel(total_user_id,total_item_id).to(device)\n",
    "optimizer = torch.optim.Adam(net.parameters(), lr = 0.1)\n",
    "for e in range(15):\n",
    "    predictions = net(batch_users_tensor,batch_items_tensor)\n",
    "    loss = loss_fn(predictions, batch_ratings_tensor)\n",
    "    print(loss)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CYeBlERRUUG0"
   },
   "outputs": [],
   "source": [
    "def shuffle(*arrays):\n",
    "\n",
    "    random_state = np.random.RandomState()\n",
    "    shuffle_indices = np.arange(len(arrays[0]))\n",
    "    random_state.shuffle(shuffle_indices)\n",
    "\n",
    "    if len(arrays) == 1:\n",
    "        return arrays[0][shuffle_indices]\n",
    "    else:\n",
    "        return tuple(x[shuffle_indices] for x in arrays)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "OmlQTG2FUUG4"
   },
   "outputs": [],
   "source": [
    "def minibatch(batch_size, *tensors):\n",
    "\n",
    "    if len(tensors) == 1:\n",
    "        tensor = tensors[0]\n",
    "        for i in range(0, len(tensor), batch_size):\n",
    "            yield tensor[i:i + batch_size]\n",
    "    else:\n",
    "        for i in range(0, len(tensors[0]), batch_size):\n",
    "            yield tuple(x[i:i + batch_size] for x in tensors)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2ekPPr7SUUG7"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "import torch.optim as optim\n",
    "\n",
    "class FactorizationModel(object):\n",
    "    \n",
    "    def __init__(self, embedding_dim=32, n_iter=10, batch_size=256, l2=0.0,\n",
    "                 learning_rate=1e-2, device=device, net=None, num_users=None,\n",
    "                 num_items=None,random_state=None):\n",
    "        \n",
    "        self._embedding_dim = embedding_dim\n",
    "        self._n_iter = n_iter\n",
    "        self._learning_rate = learning_rate\n",
    "        self._batch_size = batch_size\n",
    "        self._l2 = l2\n",
    "        self._device = device\n",
    "        self._num_users = num_users\n",
    "        self._num_items = num_items\n",
    "        self._net = net\n",
    "        self._optimizer = None\n",
    "        self._loss_func = None\n",
    "        self._random_state = random_state or np.random.RandomState()\n",
    "             \n",
    "        \n",
    "    def _initialize(self):\n",
    "        if self._net is None:\n",
    "            self._net = DotModel(self._num_users, self._num_items, self._embedding_dim).to(self._device)\n",
    "        \n",
    "        self._optimizer = optim.Adam(\n",
    "                self._net.parameters(),\n",
    "                lr=self._learning_rate,\n",
    "                weight_decay=self._l2\n",
    "            )\n",
    "        \n",
    "        self._loss_func = regression_loss\n",
    "    \n",
    "    @property\n",
    "    def _initialized(self):\n",
    "        return self._optimizer is not None\n",
    "    \n",
    "    \n",
    "    def fit(self, user_ids, item_ids, ratings, verbose=True):\n",
    "        \n",
    "        user_ids = user_ids.astype(np.int64)\n",
    "        item_ids = item_ids.astype(np.int64)\n",
    "        \n",
    "        if not self._initialized:\n",
    "            self._initialize()\n",
    "            \n",
    "        for epoch_num in range(self._n_iter):\n",
    "            users, items, ratingss = shuffle(user_ids,\n",
    "                                            item_ids,\n",
    "                                            ratings)\n",
    "\n",
    "            user_ids_tensor = torch.from_numpy(users).to(self._device)\n",
    "            item_ids_tensor = torch.from_numpy(items).to(self._device)\n",
    "            ratings_tensor = torch.from_numpy(ratingss).to(self._device)\n",
    "            epoch_loss = 0.0\n",
    "\n",
    "            for (minibatch_num,\n",
    "                 (batch_user,\n",
    "                  batch_item,\n",
    "                  batch_rating)) in enumerate(minibatch(self._batch_size,\n",
    "                                                         user_ids_tensor,\n",
    "                                                         item_ids_tensor,\n",
    "                                                         ratings_tensor)):\n",
    "                \n",
    "                \n",
    "                # beging to be completed\n",
    "                predictions = self._net(batch_user, batch_item)\n",
    "\n",
    "                self._optimizer.zero_grad()\n",
    "                \n",
    "                loss = self._loss_func(predictions,batch_rating)\n",
    "                \n",
    "                epoch_loss = epoch_loss + loss.data.item()\n",
    "                \n",
    "                loss.backward()\n",
    "                self._optimizer.step()\n",
    "                # end to be completed\n",
    "            \n",
    "            epoch_loss = epoch_loss / (minibatch_num + 1)\n",
    "            \n",
    "            if verbose:\n",
    "                print('Epoch {}: loss_train {}'.format(epoch_num, epoch_loss))\n",
    "        \n",
    "            if np.isnan(epoch_loss) or epoch_loss == 0.0:\n",
    "                raise ValueError('Degenerate epoch loss: {}'\n",
    "                                 .format(epoch_loss))\n",
    "    \n",
    "    \n",
    "    def test(self,user_ids, item_ids, ratings):\n",
    "        self._net.train(False)\n",
    "        user_ids = user_ids.astype(np.int64)\n",
    "        item_ids = item_ids.astype(np.int64)\n",
    "        \n",
    "        user_ids_tensor = torch.from_numpy(user_ids).to(self._device)\n",
    "        item_ids_tensor = torch.from_numpy(item_ids).to(self._device)\n",
    "        ratings_tensor = torch.from_numpy(ratings).to(self._device)\n",
    "               \n",
    "        predictions = self._net(user_ids_tensor, item_ids_tensor)\n",
    "        \n",
    "        loss = self._loss_func(ratings_tensor, predictions)\n",
    "        return loss.data.item()\n",
    "\n",
    "    def predict(self,user_ids, item_ids):\n",
    "        self._net.train(False)\n",
    "        user_ids = user_ids.astype(np.int64)\n",
    "        item_ids = item_ids.astype(np.int64)\n",
    "        \n",
    "        user_ids_tensor = torch.from_numpy(user_ids).to(self._device)\n",
    "        item_ids_tensor = torch.from_numpy(item_ids).to(self._device)\n",
    "               \n",
    "        predictions = self._net(user_ids_tensor, item_ids_tensor)\n",
    "        return predictions.data   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qGuzPUNrUUG_"
   },
   "outputs": [],
   "source": [
    "model = FactorizationModel(embedding_dim=50,  # latent dimensionality\n",
    "                                   n_iter=10,  # number of epochs of training\n",
    "                                   batch_size=1024,  # minibatch size\n",
    "                                   learning_rate=1e-3,\n",
    "                                   l2=1e-9,  # strength of L2 regularization\n",
    "                                   num_users=total_user_id,\n",
    "                                   num_items=total_item_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mMKyTn4QUUHC"
   },
   "outputs": [],
   "source": [
    "user_ids_train_np = user_id_train.values.astype(np.int32)\n",
    "item_ids_train_np = item_id_train.values.astype(np.int32)\n",
    "ratings_train_np = rating_train.values.astype(np.float32)\n",
    "user_ids_test_np = user_id_test.values.astype(np.int64)\n",
    "item_ids_test_np = item_id_test.values.astype(np.int64)\n",
    "ratings_test_np = rating_test.values.astype(np.float32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2Bd2CIosUUHJ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0: loss_train 13.329364981832384\n",
      "Epoch 1: loss_train 11.680887101571772\n",
      "Epoch 2: loss_train 6.6978797852238525\n",
      "Epoch 3: loss_train 2.4485379155678086\n",
      "Epoch 4: loss_train 1.32641437989247\n",
      "Epoch 5: loss_train 1.0709657427630848\n",
      "Epoch 6: loss_train 0.9698449667496017\n",
      "Epoch 7: loss_train 0.9166013628621644\n",
      "Epoch 8: loss_train 0.887195759936224\n",
      "Epoch 9: loss_train 0.8678799867630005\n"
     ]
    }
   ],
   "source": [
    "model.fit(user_ids_train_np, item_ids_train_np, ratings_train_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HKyc7Bg5UUHR"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9226775169372559"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.test(user_ids_test_np, item_ids_test_np, ratings_test_np  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "W9BIV9WaUUHN"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DotModel(\n",
      "  (user_embeddings): ScaledEmbedding(943, 50)\n",
      "  (item_embeddings): ScaledEmbedding(1682, 50)\n",
      "  (user_biases): ZeroEmbedding(943, 1)\n",
      "  (item_biases): ZeroEmbedding(1682, 1)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "print(model._net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ltvw1o_dVjbm"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final test RMSE: 0.961\n",
      "Final test MAE: 0.756\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "test_preds = model.predict(user_ids_test_np, item_ids_test_np)\n",
    "print(\"Final test RMSE: %0.3f\" % np.sqrt(mean_squared_error(test_preds.cpu(), ratings_test_np)))\n",
    "print(\"Final test MAE: %0.3f\" % mean_absolute_error(test_preds.cpu(), ratings_test_np))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yXVIF75IUUHW"
   },
   "source": [
    "You can compare with [Surprise](https://github.com/NicolasHug/Surprise)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MwQ3gXyuUUHY"
   },
   "source": [
    "## 4. Best and worst movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RxwaryZZUUHY"
   },
   "source": [
    "Getting the name of the movies (there must be a better way, please provide alternate solutions!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "I-hyp9IpUUHa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  item_id          item_name\n",
       "0       1   Toy Story (1995)\n",
       "1       2   GoldenEye (1995)\n",
       "2       3  Four Rooms (1995)\n",
       "3       4  Get Shorty (1995)\n",
       "4       5     Copycat (1995)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_movies_names = []\n",
    "list_item_ids = []\n",
    "with open(op.join(ML_100K_FOLDER, 'u.item'), encoding = \"ISO-8859-1\") as fp:\n",
    "    for line in fp:\n",
    "        list_item_ids.append(line.split('|')[0])\n",
    "        list_movies_names.append(line.split('|')[1])\n",
    "        \n",
    "movies_names = pd.DataFrame(list(zip(list_item_ids, list_movies_names)), \n",
    "               columns =['item_id', 'item_name']) \n",
    "movies_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B8aAYBuhUUHf"
   },
   "outputs": [],
   "source": [
    "item_bias_np = model._net.item_biases.weight.data.cpu().numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZhtanoCiUUHi"
   },
   "outputs": [],
   "source": [
    "movies_names['biases'] = pd.Series(item_bias_np.T[0], index=movies_names.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "2TAx4Tz6UUHk"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "      <th>biases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Toy Story (1995)</td>\n",
       "      <td>0.207793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>GoldenEye (1995)</td>\n",
       "      <td>0.163097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Four Rooms (1995)</td>\n",
       "      <td>0.157520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Get Shorty (1995)</td>\n",
       "      <td>0.183892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Copycat (1995)</td>\n",
       "      <td>0.176081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  item_id          item_name    biases\n",
       "0       1   Toy Story (1995)  0.207793\n",
       "1       2   GoldenEye (1995)  0.163097\n",
       "2       3  Four Rooms (1995)  0.157520\n",
       "3       4  Get Shorty (1995)  0.183892\n",
       "4       5     Copycat (1995)  0.176081"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_names.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gn8gcmQjUUHo"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1682, 3)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_names.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Q6ZmJYIdUUHt"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1656, 3)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices_item_train = np.sort(item_id_train.unique())\n",
    "movies_names = movies_names.loc[indices_item_train]\n",
    "movies_names.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iUqELsLPUUH1"
   },
   "outputs": [],
   "source": [
    "movies_names = movies_names.sort_values(ascending=False,by=['biases'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7TG247DXUUH4"
   },
   "source": [
    "Best movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZzQGi5VIUUH5"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "      <th>biases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>302</td>\n",
       "      <td>L.A. Confidential (1997)</td>\n",
       "      <td>0.239796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>272</td>\n",
       "      <td>Good Will Hunting (1997)</td>\n",
       "      <td>0.234269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>269</td>\n",
       "      <td>Full Monty, The (1997)</td>\n",
       "      <td>0.231962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>408</td>\n",
       "      <td>Close Shave, A (1995)</td>\n",
       "      <td>0.230694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>515</td>\n",
       "      <td>Boot, Das (1981)</td>\n",
       "      <td>0.229575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>242</td>\n",
       "      <td>Kolya (1996)</td>\n",
       "      <td>0.228935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>313</td>\n",
       "      <td>Titanic (1997)</td>\n",
       "      <td>0.228393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>483</td>\n",
       "      <td>Casablanca (1942)</td>\n",
       "      <td>0.226952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>315</td>\n",
       "      <td>Apt Pupil (1998)</td>\n",
       "      <td>0.224810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>Hoop Dreams (1994)</td>\n",
       "      <td>0.224387</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    item_id                 item_name    biases\n",
       "301     302  L.A. Confidential (1997)  0.239796\n",
       "271     272  Good Will Hunting (1997)  0.234269\n",
       "268     269    Full Monty, The (1997)  0.231962\n",
       "407     408     Close Shave, A (1995)  0.230694\n",
       "514     515          Boot, Das (1981)  0.229575\n",
       "241     242              Kolya (1996)  0.228935\n",
       "312     313            Titanic (1997)  0.228393\n",
       "482     483         Casablanca (1942)  0.226952\n",
       "314     315          Apt Pupil (1998)  0.224810\n",
       "47       48        Hoop Dreams (1994)  0.224387"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_names.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y3fHTO-eUUH_"
   },
   "source": [
    "Worse movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1-EhFYOwUUIA"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>item_id</th>\n",
       "      <th>item_name</th>\n",
       "      <th>biases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>851</th>\n",
       "      <td>852</td>\n",
       "      <td>Bloody Child, The (1996)</td>\n",
       "      <td>0.040371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>830</td>\n",
       "      <td>Power 98 (1995)</td>\n",
       "      <td>0.037940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>1309</td>\n",
       "      <td>Very Natural Thing, A (1974)</td>\n",
       "      <td>0.037134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1660</th>\n",
       "      <td>1661</td>\n",
       "      <td>New Age, The (1994)</td>\n",
       "      <td>0.037113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1493</th>\n",
       "      <td>1494</td>\n",
       "      <td>Mostro, Il (1994)</td>\n",
       "      <td>0.036677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1620</th>\n",
       "      <td>1621</td>\n",
       "      <td>Butterfly Kiss (1995)</td>\n",
       "      <td>0.035107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>1304</td>\n",
       "      <td>New York Cop (1996)</td>\n",
       "      <td>0.034351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1485</th>\n",
       "      <td>1486</td>\n",
       "      <td>Girl in the Cadillac (1995)</td>\n",
       "      <td>0.032871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>599</td>\n",
       "      <td>Police Story 4: Project S (Chao ji ji hua) (1993)</td>\n",
       "      <td>0.032455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1595</th>\n",
       "      <td>1596</td>\n",
       "      <td>Nemesis 2: Nebula (1995)</td>\n",
       "      <td>0.025879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     item_id                                          item_name    biases\n",
       "851      852                           Bloody Child, The (1996)  0.040371\n",
       "829      830                                    Power 98 (1995)  0.037940\n",
       "1308    1309                       Very Natural Thing, A (1974)  0.037134\n",
       "1660    1661                                New Age, The (1994)  0.037113\n",
       "1493    1494                                  Mostro, Il (1994)  0.036677\n",
       "1620    1621                              Butterfly Kiss (1995)  0.035107\n",
       "1303    1304                                New York Cop (1996)  0.034351\n",
       "1485    1486                        Girl in the Cadillac (1995)  0.032871\n",
       "598      599  Police Story 4: Project S (Chao ji ji hua) (1993)  0.032455\n",
       "1595    1596                           Nemesis 2: Nebula (1995)  0.025879"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies_names.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t_CPwQ2bj64P"
   },
   "source": [
    "## 5. PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4WB_IZtPj64P"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1682, 50)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "item_emb_np = model._net.item_embeddings.weight.data.cpu().numpy()\n",
    "item_emb_np.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Uqe-QXDJj64R"
   },
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from operator import itemgetter\n",
    "\n",
    "pca = PCA(n_components=3)\n",
    "latent_fac = pca.fit_transform(item_emb_np)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SArVAj3tj64U"
   },
   "outputs": [],
   "source": [
    "movie_comp = [(f, i) for f,i in zip(latent_fac[:,1], list_movies_names)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ZC9L-xPcj64V"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.62616926, 'Liar Liar (1997)'),\n",
       " (0.57997584, 'Air Force One (1997)'),\n",
       " (0.54750156, 'Titanic (1997)'),\n",
       " (0.5310536, \"Dante's Peak (1997)\"),\n",
       " (0.50612336, 'Independence Day (ID4) (1996)'),\n",
       " (0.49544817, 'Murder at 1600 (1997)'),\n",
       " (0.49176452, 'Twister (1996)'),\n",
       " (0.48357046, 'Conspiracy Theory (1997)'),\n",
       " (0.48287913, 'Jungle2Jungle (1997)'),\n",
       " (0.47478956, 'Ransom (1996)')]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=True)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ua6iU6TRj64W"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(-0.3254464, 'Maltese Falcon, The (1941)'),\n",
       " (-0.32396626, 'M (1931)'),\n",
       " (-0.32287025,\n",
       "  'Dr. Strangelove or: How I Learned to Stop Worrying and Love the Bomb (1963)'),\n",
       " (-0.31263804, 'Laura (1944)'),\n",
       " (-0.31038454, 'Spellbound (1945)'),\n",
       " (-0.2967051, 'Ben-Hur (1959)'),\n",
       " (-0.29528522, 'Old Man and the Sea, The (1958)'),\n",
       " (-0.29361802, 'Murder, My Sweet (1944)'),\n",
       " (-0.29214993, 'Party Girl (1995)'),\n",
       " (-0.28984538, 'Bullets Over Broadway (1994)')]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(movie_comp, key=itemgetter(0), reverse=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uWyiIsPvj64X"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Star Wars (1977)', 'Contact (1997)', 'Fargo (1996)',\n",
       "       'Return of the Jedi (1983)', 'Liar Liar (1997)',\n",
       "       'English Patient, The (1996)', 'Scream (1996)', 'Toy Story (1995)',\n",
       "       'Air Force One (1997)', 'Independence Day (ID4) (1996)'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = all_ratings.groupby('item_name')['ratings'].count()\n",
    "most_rated_movies = g.sort_values(ascending=False).index.values[:1000]\n",
    "most_rated_movies[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zWAKDHvEj64Y"
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "idxs = range(50)\n",
    "txt_movies_names = most_rated_movies[:len(idxs)]\n",
    "X = latent_fac[idxs,0]\n",
    "Y = latent_fac[idxs,2]\n",
    "plt.figure(figsize=(15,15))\n",
    "plt.scatter(X, Y)\n",
    "for i, x, y in zip(txt_movies_names, X, Y):\n",
    "    plt.text(x+0.01,y-0.01,i, fontsize=11)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XwOYSxcZUUII"
   },
   "source": [
    "## 6. SPOTLIGHT\n",
    "\n",
    "The code written above is a simplified version of [SPOTLIGHT](https://github.com/maciejkula/spotlight)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-nPXmJ_nUUIJ"
   },
   "source": [
    "Once you installed it with: `conda install -c maciejkula -c pytorch spotlight=0.1.5`, you can compare the results..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aCEqMg9WUUIK"
   },
   "source": [
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